An image processing based paradigm for the extraction of tonal sounds in cetacean communications

Automated extraction of baleen whale calls based on the pseudo-Wigner-Ville distribution

Baleen whales produce a wide variety of frequency-modulated calls. Extraction of the time-frequency (TF) structures of these calls forms the basis for many applications, including abundance estimation and species recognition. Typical methods to extract the contours of whale calls from a spectrogram are based on the short-time Fourier transform and are, thus, restricted by a fixed TF resolution. Considering the low-frequency nature of baleen whale calls, this work represents the contours using a pseudo-Wigner-Ville distribution for a higher TF resolution at the cost of introducing cross terms. An adaptive threshold is proposed followed by a modified Gaussian mixture probability hypothesis density filter to extract the contours. Finally, the artificial contours, which are caused by the cross terms, can be removed in post-processing. Simulations were conducted to explore how the signal-to-noise ratio influences the performance of the proposed method. Then, in experiments based on real data, the contours of the calls of three kinds of baleen whales were extracted in a highly accurate manner (with mean deviations of 5.4 and 0.051 Hz from the ground-truth contours at sampling rates of 4000 and 100 Hz, respectively) with a recall of 75% and a precision of 78.5%.

Silbido profundo: An open source package for the use of deep learning to detect odontocete whistles

This work presents an open-source matlab software package for exploiting recent advances in extracting tonal signals from large acoustic data sets. A whistle extraction algorithm published by Li, Liu, Palmer, Fleishman, Gillespie, Nosal, Shiu, Klinck, Cholewiak, Helble, and Roch [(2020). Proceedings of the International Joint Conference on Neural Networks, July 19-24, Glasgow, Scotland, p. 10] is incorporated into silbido, an established software package for extraction of cetacean tonal calls. The precision and recall of the new system were over 96% and nearly 80%, respectively, when applied to a whistle extraction task on a challenging two-species subset of a conference-benchmark data set. A second data set was examined to assess whether the algorithm generalized to data that were collected across different recording devices and locations. These data included 487 h of weakly labeled, towed array data collected in the Pacific Ocean on two National Oceanographic and Atmospheric Administration (NOAA) cruises. Labels for these data consisted of regions of toothed whale presence for at least 15 species that were based on visual and acoustic observations and not limited to whistles. Although the lack of per whistle-level annotations prevented measurement of precision and recall, there was strong concurrence of automatic detections and the NOAA annotations, suggesting that the algorithm generalizes well to new data.

Automatic detectors for low-frequency vocalizations of Omura's whales, Balaenoptera omurai: A performance comparison

Automatically detecting animal signals in soundscape recordings is of benefit to passive acoustic monitoring programs which may be undertaken for research or conservation. Numerous algorithms exist, which are typically optimized for certain situations (i.e., certain animal sound types and ambient noise conditions). Adding to the library of algorithms, this paper developed, tested, and compared three detectors for Omura's whale vocalizations (15-62 Hz; <15 s) in marine soundscape recordings which contained noise from other animals, wind, earthquakes, ships, and seismic surveys. All three detectors were based on processing of spectrographic representations. The specific methods were spectrogram cross-correlation, entropy computation, and spectral intensity "blob" tracing. The latter two were general-purpose detectors that were adapted for detection of Omura's whale vocalizations. Detector complexity and post-processing effort varied across the three detectors. Performance was assessed qualitatively using demonstrative examples, and quantitatively using Receiver-Operating Characteristics and Precision-Recall curves. While the results of quantitative assessment were dominated by the spectrogram cross-correlation method, qualitative assessment showed that all three detectors offered promising performance.

A performance comparison of tonal detectors for low-frequency vocalizations of Antarctic blue whales

Extraction of tonal signals embedded in background noise is a crucial step before classification and separation of low-frequency sounds of baleen whales. This work reports results of comparing five tonal detectors, namely the instantaneous frequency estimator, YIN estimator, harmonic product spectrum, cost-function-based detector, and ridge detector. Comparisons, based on a low-frequency adaptation of the Silbido scoring feature, employ five metrics, which quantify the effectiveness of these detectors to retrieve tonal signals that have a wide range of signal to noise ratios (SNRs) and the quality of the detection results. Ground-truth data were generated by embedding 20 synthetic Antarctic blue whale (Balaenoptera musculus intermedia) calls in randomly extracted 30-min noise segments from a 79 h-library recorded by an Ocean Bottom Seismometer in the Indian Ocean during 2012-2013. Monte-Carlo simulations were performed using 20 trials per SNR, ranging from 0 dB to 15 dB. Overall, the tonal detection results show the superiority of the cost-function-based and the ridge detectors, over the other detectors, for all SNR values. More particularly, for lower SNRs (⩽3 dB), these two methods outperformed the other three with high recall, low fragmentation, and high coverage scores. For SNRs ⩾7 dB, the five methods performed similarly.

Tracking cryptic animals using acoustic multilateration: A system for long-range wolf detection

The study of animal behavior in the wild requires the ability to locate and observe animals with the minimum disturbance to their natural behavior. This can be challenging for animals that avoid humans, are difficult to detect, or range widely between sightings. Global Positioning System (GPS) collars provide one solution but limited battery life, and the disturbance to the animal caused by capture and collaring can make this impractical in many applications. Wild wolves Canis lupus are an example of a species that is difficult to study in the wild, yet are of considerable conservation and management importance. This manuscript presents a system for accurately locating wolves using differences in the time of arrival of howl vocalizations at multiple recorders (multilateration), synchronized via GPS. This system has been deployed in Yellowstone National Park for two years and has recorded over 1200 instances of howling behavior. As most instances of howling occur at night, or when human observers are not physically present, the system provides location information that would otherwise be unavailable to researchers. The location of a vocalizing animal can, under some circumstances, be determined to within an error of approximately 20 m and at ranges up to 7 km.

Automated tracking of dolphin whistles using Gaussian mixture probability hypothesis density filters

This work considers automated multi target tracking of odontocete whistle contours. An adaptation of the Gaussian mixture probability hypothesis density (GM-PHD) filter is described and applied to the acoustic recordings from six odontocete species. From the raw data, spectral peaks are first identified and then the GM-PHD filter is used to simultaneously track the whistles' frequency contours. Overall over 9000 whistles are tracked with a precision of 85% and recall of 71.8%. The proposed filter is shown to track whistles precisely (with mean deviation of 104 Hz, about one frequency bin, from the annotated whistle path) and 80% coverage. The filter is computationally efficient, suitable for real-time implementation, and is widely applicable to different odontocete species.

Acoustic sequences in non-human animals: a tutorial review and prospectus

Animal acoustic communication often takes the form of complex sequences, made up of multiple distinct acoustic units. Apart from the well-known example of birdsong, other animals such as insects, amphibians, and mammals (including bats, rodents, primates, and cetaceans) also generate complex acoustic sequences. Occasionally, such as with birdsong, the adaptive role of these sequences seems clear (e.g. mate attraction and territorial defence). More often however, researchers have only begun to characterise - let alone understand - the significance and meaning of acoustic sequences. Hypotheses abound, but there is little agreement as to how sequences should be defined and analysed. Our review aims to outline suitable methods for testing these hypotheses, and to describe the major limitations to our current and near-future knowledge on questions of acoustic sequences. This review and prospectus is the result of a collaborative effort between 43 scientists from the fields of animal behaviour, ecology and evolution, signal processing, machine learning, quantitative linguistics, and information theory, who gathered for a 2013 workshop entitled, 'Analysing vocal sequences in animals'. Our goal is to present not just a review of the state of the art, but to propose a methodological framework that summarises what we suggest are the best practices for research in this field, across taxa and across disciplines. We also provide a tutorial-style introduction to some of the most promising algorithmic approaches for analysing sequences. We divide our review into three sections: identifying the distinct units of an acoustic sequence, describing the different ways that information can be contained within a sequence, and analysing the structure of that sequence. Each of these sections is further subdivided to address the key questions and approaches in that area. We propose a uniform, systematic, and comprehensive approach to studying sequences, with the goal of clarifying research terms used in different fields, and facilitating collaboration and comparative studies. Allowing greater interdisciplinary collaboration will facilitate the investigation of many important questions in the evolution of communication and sociality.